Article ; Online: Automatic generation of conclusions from neuroradiology MRI reports through natural language processing.
2024 Volume 66, Issue 4, Page(s) 477–485
Abstract: Purpose: The conclusion section of a radiology report is crucial for summarizing the primary radiological findings in natural language and essential for communicating results to clinicians. However, creating these summaries is time-consuming, repetitive, ...
| Abstract | Purpose: The conclusion section of a radiology report is crucial for summarizing the primary radiological findings in natural language and essential for communicating results to clinicians. However, creating these summaries is time-consuming, repetitive, and prone to variability and errors among different radiologists. To address these issues, we evaluated a fine-tuned Text-To-Text Transfer Transformer (T5) model for abstractive summarization to automatically generate conclusions for neuroradiology MRI reports in a low-resource language. Methods: We retrospectively applied our method to a dataset of 232,425 neuroradiology MRI reports in Spanish. We compared various pre-trained T5 models, including multilingual T5 and those newly adapted for Spanish. For precise evaluation, we employed BLEU, METEOR, ROUGE-L, CIDEr, and cosine similarity metrics alongside expert radiologist assessments. Results: The findings are promising, with the models specifically fine-tuned for neuroradiology MRI achieving scores of 0.46, 0.28, 0.52, 2.45, and 0.87 in the BLEU-1, METEOR, ROUGE-L, CIDEr, and cosine similarity metrics, respectively. In the radiological experts' evaluation, they found that in 75% of the cases evaluated, the conclusions generated by the system were as good as or even better than the manually generated conclusions. Conclusion: The methods demonstrate the potential and effectiveness of customizing state-of-the-art pre-trained models for neuroradiology, yielding automatic MRI report conclusions that nearly match expert quality. Furthermore, these results underscore the importance of designing and pre-training a dedicated language model for radiology report summarization. |
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| MeSH term(s) | Humans ; Natural Language Processing ; Retrospective Studies ; Language ; Magnetic Resonance Imaging ; Radiology |
| Language | English |
| Publishing date | 2024-02-21 |
| Publishing country | Germany |
| Document type | Journal Article |
| ZDB-ID | 123305-1 |
| ISSN | 1432-1920 ; 0028-3940 |
| ISSN (online) | 1432-1920 |
| ISSN | 0028-3940 |
| DOI | 10.1007/s00234-024-03312-3 |
| Database | MEDical Literature Analysis and Retrieval System OnLINE |
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